Resting Heart Rate and Heart Rate Variability (HRV): What’s the Difference? — Part 3

In part 1 of this series, I covered the basic physiology of heart rhythm regulation. In part 2, I discussed the technology required for these measurements, why some sensors can be trusted, and why others can be used just for resting heart rate, and not for HRV. In this blog, we finally start looking at the data.

In particular, I will cover population-level differences in resting heart rate and HRV, what factors consistently show changes in resting physiology, and what we can derive from this type of analysis. Needless to say, both resting heart rate and HRV become a lot more useful when tracked over time within individuals. However, that’s something for the following post.

Let’s discuss absolute values and differences between subgroups of the population first.

You can find the other parts of this series at these links:

Check out my Twitter (@altini_marco) for updates.

Population-level differences

Hopefully, we have a bit more clarity on the physiology of resting heart rate vs HRV as well as potential issues with available technologies. In this section we dive deeper into the data, highlighting important, quantifiable differences in both resting heart rate and HRV at the population level (differences between people).

Normally, I’m not a fan of population-level analysis. I find it a bit of an outdated way of looking at physiological data (getting some people in the lab, taking a few measurements, looking at differences between people without any concern for their historical data, context, changes over time, etc.). For example, if you measure an athlete’s resting heart rate while sick, it might be 55 bpm, instead of the usual 40 bpm. According to “population averages”, nothing is wrong, and yet we know this is far from the truth.

While longitudinal measurement is the way to go forward (analyzing changes in your physiology with respect to your historical data, to highlight periods of higher stress or abnormalities), looking at population averages and stratifying such averages across different groups of individuals (men/women, based on activity level, age groups, etc.) can still provide some useful insights on the differences between resting heart rate and HRV. Our goal here is really to better grasp these differences, and therefore this analysis becomes useful even at the population level.

Let’s look at some of these relationships.


Differences in resting heart rate and HRV between men and women have been documented in the literature for many years. Similarly, wearables and apps have backed up insights from smaller studies, showing consistent differences in some of these parameters.

Since women tend to be smaller than men, the heart is also smaller, and therefore stroke volume is lower, meaning that a slightly higher heart rate is required to provide the same amount of blood to the rest of the body. A similar principle results in lower heart rates when adapting to training, as the heart becomes larger, stroke volume increases, and then the cardiac output can be the same at a lower heart rate (something for the next section).

But what about HRV? It follows from the inverse relationship between resting heart rate and HRV, that normally we should expect HRV to be slightly lower in women. According to literature, this is not necessarily the case, as in some studies women have been reported as having higher HRV than men until menopause, indicating a potential role of hormones in driving some of these differences. Yet, other wearables have reported a slightly higher HRV for men, while you can see from our data below that we do not see consistent differences between men and women when it comes to HRV. Some of these inconsistencies can probably be explained by other confounding factors (some will be explored below, such as age, body mass index, and fitness level, as well as other stressors).

Below is HRV4Training data. Note that here we are not breaking it down by age or fitness level just yet, which means the two populations (men and women) could have different characteristics. However, we can see quite clearly the difference in resting heart rate, while HRV seems to overlap almost perfectly.

According to this data, which of course covers a sample of the population that tends to be active and/or more health-conscious than the average person, the median heart rate for men is 55 bpm, while for women is 59 bpm. In terms of HRV, the median rMSSD is 60 ms for both men and women.

To summarize:

  • Population data covers a huge range, highlighting how looking at your own individual variability over time becomes more relevant for decision making. This is the case for both resting heart rate and HRV, but especially for HRV, due to the broader range of possible values.
  • A slightly higher resting heart rate in women is well documented and results from differences in the size of the heart.
  • HRV is fairly similar between men and women, with potential differences disappearing after menopause.

Cardiorespiratory fitness

We all know athletes have lower resting heart rates than the rest of the population. This is a well-known training adaptation, as the heart muscle (and left ventricle) increases in size, stroke volume also increases, and therefore the same cardiac output (blood pumped through your body) can be sustained with a lower heart rate.

What about HRV? Some studies have shown increases in HRV following a physical activity intervention. However, I think it is always important to contextualize these changes with respect to the target population. While larger amounts of low intensity (below “aerobic threshold”) training seem beneficial and can result also in improved HRV, normally in the context of HRV what we try to optimize is performance outcomes, not HRV itself. HRV as a marker of stress becomes useful for continuous feedback and day-to-day adjustments, not so much as a marker of our progress with training. Or in other words, we do not really expect HRV to increase over time as we become more fit. This of course does not mean that HRV cannot increase (or better, improve, meaning getting either a little higher or more stable over time), but simply that an increase in HRV is not an expected outcome because genetics and age drive most of the “absolute value” in HRV. As Stephen Seiler put it recently, “HRV should be seen more as a good marker of an overall «fit and not too stressed» state of the organism rather than a primary training outcome (like 20 min power or something similar)”.

It follows from these considerations that we should see clear differences in resting heart rate based on “training status” or cardiorespiratory fitness level, while most likely HRV will show a weak association.

This is something we have also investigated in the context of estimating VO2max (or in other words, cardiorespiratory fitness level), showing how submaximal heart rate during exercise is a great predictor, how resting heart rate can still be helpful, and how HRV is in fact of little use in discriminating individual variability in fitness level.

Below is again some data in which we clustered individuals simply based on the numbers of workouts performed weekly, which is just a rough categorization associated with fitness level. Yet, the relationships just covered are clearly visible.

In the figures above, the median heart rate is 54 bpm for individuals training daily, and 65 bpm for individuals training occasionally. On the other hand, the median rMSSD is 66 ms for individuals training daily, and 48 ms for individuals training occasionally. The explained variance of training frequency for resting heart rate is 10%, while it is only 5% for rMSSD, which simply means there is a stronger link between resting heart rate and cardiorespiratory fitness than there is for HRV.

To summarize:

  • Due to well-known training adaptations, resting heart rate decreases with increased cardiorespiratory fitness level
  • HRV shows a weaker association with fitness level and is less predictive of changes in fitness.

Put it another way, it is extremely unlikely for an elite athlete not to have a low resting heart rate. However, it is not that uncommon for an elite athlete to have a relatively low HRV, with respect to the population.


The relationship between resting physiology and aging gets more interesting, and once again shows how resting heart rate and HRV differ. In particular, so far we have seen how sex and cardiorespiratory fitness level are better captured by resting heart rate, with respect to HRV.

When it comes to aging, HRV becomes the key marker.

We can see below in HRV4Training data how resting heart rate does not seem to change as we age, while there is a large reduction in HRV. Reduced HRV with age could be a reflection of impaired autonomic activity, and our reduced capacity to respond to the different stressors we face (environmental or other), which should come with no surprise.

In this case, the median resting heart rate is exactly the same across all age groups between 20 and 60 years old (56 beats per minute). On the other hand, rMSSD for the 20–30 age group is 78 ms, while for the 50–60 age group is 46 ms, a large reduction. Age alone can explain 12% of the variance in HRV at the population level, while it explains none (0%) of the variance in resting heart rate in this data.

To summarize:

  • HRV is tightly coupled to aging and could be a marker of aging, as shown by the association between e.g. lower HRV and negative health outcomes.
  • Resting heart rate shows no link to aging, and most likely reflects changes associated with training habits, more than other factors.

Body mass (index)

As the last parameter, we will look at body mass index (BMI). In this case, I have filtered data to keep only the two BMI categories with the most data, which are in our case what is considered normal (18.5–25) and what is considered overweight (25–30). Plenty of research has shown negative associations between resting physiology and being overweight (lowered HRV, increased resting heart rate), and this is something that can be easily confirmed when looking at HRV4Training data.

BMI is probably the one parameter where resting heart rate and HRV provide similar insights:

In particular, we have that the median resting heart rate is 55 bpm for the normal BMI category, and 58 bpm for the overweight BMI category. On the other hand, we have that the median rMSSD is 63 ms for the normal BMI category, and 56 ms for the overweight BMI category.

To summarize:

  • Higher BMI is associated with lower HRV and higher resting heart rate, with comparable differences between the two metrics.

Putting it all together

Given the relatively small differences due to sex, and comparable changes in resting heart rate and HRV when looking at BMI categories, in the figure below I chose the parameters that seem the most interesting at the population level: age and physical activity level.

These are the parameters where we can see the largest differences, and where we can potentially better understand associations between resting heart rate, HRV, and health outcomes as well as differences between resting heart rate and HRV.

Once again, below we can see how resting heart rate does not change across age groups but is tightly coupled to physical activity level. On the other hand, we can see how HRV reduces with age no matter the physical activity level, even though slightly higher HRV is associated with higher fitness across age groups.

Combining all the parameters above (sex, age, training frequency, and body mass index), the explained variance for heart rate is about 18%, and for HRV is about 15%. This is exactly why it makes little sense to compare your resting physiology to others, as much of the differences between individuals are not captured by behavioral or easy to determine characteristics (but could be for example due to genetics, or other factors).

While absolute values and differences across subgroups of the population are informative to better grasp differences in the processes captured by resting heart rate and HRV, we will see in the next part of this series of resting physiology becomes more informative when looking at individual changes over time.


Needless to say, both HR and HRV are key health and performance markers. But as this series tries to point out from different angles, they do differ (which is why it is worth looking at both).

The differences in resting heart rate and HRV are linked to their physiological origin and can be observed at the population level when looking at different subgroups of the population, as I have reported in this blog.

We have seen in part 1 of this series how when we count beats over a period of time (that is, resting heart rate), we completely ignore the timing of parasympathetic influence on heart rhythm and therefore miss key information. This explains why parasympathetic activity is better captured by HRV, and why HRV is in turn associated with aging. On the other hand, structural changes in the heart muscle explain the strong association between resting heart rate and cardiorespiratory fitness, which has less to do with autonomic modulation and parasympathetic activity.

While it is key to start from solid theoretical foundations, the ability of current technology to capture physiological changes in response to stressors as well as other physiological adaptations, is what makes these parameters useful.

We will see in the next section on individual-level analysis how resting heart rate and HRV become even more insightful in the context of stress responses.

Stay tuned.

Marco holds a PhD cum laude in applied machine learning, a M.Sc. cum laude in computer science engineering, and a M.Sc. cum laude in human movement sciences and high-performance coaching.

He has published more than 50 papers and patents at the intersection between physiology, health, technology, and human performance.

Marco is the founder of HRV4Training, data science advisor at Oura, and guest lecturer at VU Amsterdam. He loves running.

Twitter: @altini_marco

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